Data Learning: Integrating Data Assimilation and Machine Learning

Journal of Computational Science - Tập 58 - Trang 101525 - 2022
Caterina Buizza1, César Quilodrán Casas2, Philip Nadler2, Julian Mack2, Stefano Marrone2,3, Zainab Titus4, Clémence Le Cornec5, Evelyn Heylen6, Tolga Dur2, Luis Baca Ruiz2,7, Claire Heaney4, Julio Amador Díaz Lopez2,8, K.S. Sesh Kumar2, Rossella Arcucci2,4
1Personal Robotics Lab, Department of EEE, Imperial College London, UK
2Data Science Institute, Imperial College London, UK
3DIETI, University of Naples “Federico II”, Italy
4Department of Earth Science and Engineering, Imperial College, London, UK
5Department of Civil and Environmental Engineering, Imperial College, London, UK
6Control and Power Group, Department of EEE, Imperial College London, UK
7Department of Computer Science and Artificial Intelligence, University of Granada, Spain
8Data Science Intitute, London School of Economics and Political Science, UK

Tài liệu tham khảo

Andriluka, 2018, PoseTrack: a benchmark for human pose estimation and tracking, IEEE Conference on Computer Vision and Pattern Recognition, 5167

Arcucci, 2017, On the variational data assimilation problem solving and sensitivity analysis, J. Comput. Phys., 335, 311, 10.1016/j.jcp.2017.01.034

Arcucci, 2019, Optimal reduced space for variational data assimilation, J. Comput. Phys., 379, 51, 10.1016/j.jcp.2018.10.042

Arcucci, 2020, Neural assimilation, International Conference on Computational Science, 155

Arcucci, 2021, Deep data assimilation: integrating deep learning with data assimilation, Appl. Sci., 11, 1114, 10.3390/app11031114

Asch, 2016

Bishop, 2006

Bocquet, 2020, Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization, Found. Data Sci., 2, 55, 10.3934/fods.2020004

Bonavita, 2020, Machine learning for earth system observation and prediction, Bull. Am. Meteorol. Soc., 1

Bonavita, 2014

Boukabara, 2019, Leveraging modern artificial intelligence for remote sensing and nwp: benefits and challenges, Bull. Am. Meteorol. Soc., 10.1175/BAMS-D-18-0324.1

Brajard, 2019, Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model, Geosci. Model Dev. Discuss., 1

Brajard, 2020

Buizza, 2020, Real-time multi-person pose tracking using data assimilation, IEEE Winter Conference on Applications of Computer Vision, 1

Cacuci, 2005

Canchumuni, 2017, Integration of ensemble data assimilation and deep learning for history matching facies models, Offshore Technology Conference, Offshore Technology Conference, 1

Canchumuni, 2019, Towards a robust parameterization for conditioning facies models using deep variational autoencoders and ensemble smoother, Comput. Geosci., 128, 87, 10.1016/j.cageo.2019.04.006

Canova, 2009, Structural changes in the us economy: is there a role for monetary policy?, J. Econ. Dyn. Control, 33, 477, 10.1016/j.jedc.2008.05.010

Cao, 2017, Realtime multi-person 2d pose estimation using part affinity fields, IEEE Conference on Computer Vision and Pattern Recognition, 7291

Chao, 2019, Recent advances in supervised dimension reduction: a survey, Mach. Learn. Knowl. Extract., 1, 341, 10.3390/make1010020

Chen, 2018, Neural ordinary differential equations, Advances in Neural Information Processing Systems, 6571

Coskun, 2017, Long short-term memory Kalman filters: recurrent neural estimators for pose regularization, IEEE International Conference on Computer Vision, 5525

Costa, 2015, Assimilation of sea-level anomalies and Argo data into HYCOM and its impact on the 24 hour forecasts in the western tropical and South Atlantic, J. Oper. Oceanogr., 8, 52

Courtier, 1994, A strategy for operational implementation of 4d-var, using an incremental approach, Q. J. R. Meteorol. Soc., 120, 1367, 10.1002/qj.49712051912

Düben, 2021

Dueben, 2018, Challenges and design choices for global weather and climate models based on machine learning, Geosci. Model Dev., 11, 3999, 10.5194/gmd-11-3999-2018

Emerick, 2018, Deterministic ensemble smoother with multiple data assimilation as an alternative for history-matching seismic data, Comput. Geosci., 22, 1175, 10.1007/s10596-018-9745-5

Fei-Fei, 2006, One-shot learning of object categories, IEEE Trans. Pattern Anal. Mach. Intell., 28, 594, 10.1109/TPAMI.2006.79

Frolov, 2009, Fast data assimilation using a nonlinear Kalman filter and a model surrogate: an application to the Columbia River estuary, Dyn. Atmos. Oceans, 48, 16, 10.1016/j.dynatmoce.2008.10.004

Geer, 2021, Learning earth system models from observations: machine learning or data assimilation?, Philos. Trans. R. Soc. A, 379, 10.1098/rsta.2020.0089

Geer, 2020

Goodfellow, 2016

Guo, 2019, Data-driven reduced order modeling for time-dependent problems, Comput. Methods Appl. Mech. Eng., 345, 75, 10.1016/j.cma.2018.10.029

Hatfield, 2021, Building tangent-linear and adjoint models for data assimilation with neural networks, Earth Space Sci. Open Arch.

Heaney, 2018

Hesthaven, 2018, Non-intrusive reduced order modeling of nonlinear problems using neural networks, J. Comput. Phys., 363, 55, 10.1016/j.jcp.2018.02.037

Kalman, 1960, A new approach to linear filtering and prediction problems, J. Basic Eng., 82, 35, 10.1115/1.3662552

Kalnay, 2003

Khandelwal, 2020, A scalable inference method for large dynamic economic systems, NeuRIPS ML for Economic Policy Workshop

Krause, 2008, Near-optimal sensor placements in Gaussian processes: theory, efficient algorithms and empirical studies, J. Mach. Learn. Res., 9, 235

Le Cornec, 2020, Modelling of instantaneous emissions from diesel vehicles with a special focus on nox: insights from machine learning techniques, Sci. Total Environ., 737, 139625, 10.1016/j.scitotenv.2020.139625

Li, 2017, Maximum principle based algorithms for deep learning, J. Mach. Learn. Res., 18, 5998

Lin, 2019, Air quality forecast through integrated data assimilation and machine learning, International Conference on Agents and Artificial Intelligence, 787

Lorenc, 1997, Development of an operational variational assimilation scheme, J. Meteorol. Soc. Jpn., 75, 339, 10.2151/jmsj1965.75.1B_339

Ma, 2020

Mack, 2020, Attention-based convolutional autoencoders for 3d-variational data assimilation, Comput. Methods Appl. Mech. Eng., 372, 113291, 10.1016/j.cma.2020.113291

Marrone, 2017, Look-up tables for efficient non-linear parameters estimation, International Conference on Optimization and Decision Science, 49

Mosser, 2019

Nadler, 2020, A neural sir model for global forecasting, Machine Learning for Health, 254

Nadler, 2019, A scalable approach to econometric inference, PARCO, 59

Nichols, 2010

Ning, 2019

Perdikaris, 2017, Nonlinear information fusion algorithms for data-efficient multi-fidelity modelling, Proc. R. Soc. A: Math. Phys. Eng. Sci., 473, 20160751, 10.1098/rspa.2016.0751

Quilodrán Casas, 2018

Quilodrán Casas, 2020, A reduced order deep data assimilation model, Phys. D: Nonlinear Phenom., 412, 132615, 10.1016/j.physd.2020.132615

Raaj, 2019, Efficient online multi-person 2d pose tracking with recurrent spatio-temporal affinity fields, IEEE Conference on Computer Vision & Pattern Recognition, 4620

Raissi, 2016, Inferring solutions of differential equations using noisy multi-fidelity data, J. Comput. Phys., 335

Raissi, 2019, Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, J. Comput. Phys., 378, 686, 10.1016/j.jcp.2018.10.045

Rasheed, 2019

Rasmussen, 2005

Shannon, 1948, A mathematical theory of communication, Bell Syst. Techn. J., 27, 379, 10.1002/j.1538-7305.1948.tb01338.x

Skitka, 2020, Reduced-order quasilinear model of ocean boundary-layer turbulence, J. Phys. Oceanogr., 50, 537, 10.1175/JPO-D-19-0149.1

Smith, 2009, Variational data assimilation for parameter estimation: application to a simple morphodynamic model, Ocean Dyn., 59, 697, 10.1007/s10236-009-0205-6

Swischuk, 2019, Projection-based model reduction: formulations for physics-based machine learning, Comput. Fluids, 179, 704, 10.1016/j.compfluid.2018.07.021

Titsias, 2009, Variational learning of inducing variables in sparse gaussian processes, Artificial Intelligence and Statistics, 567

Tofts, 1999, Estimating kinetic parameters from dynamic contrast-enhanced t 1-weighted mri of a diffusable tracer: standardized quantities and symbols, J. Magn. Reson. Imaging, 10, 223, 10.1002/(SICI)1522-2586(199909)10:3<223::AID-JMRI2>3.0.CO;2-S

Vinyals, 2016, Matching networks for one shot learning, Adv. Neural Inform. Process. Syst., 29, 3630

Walter, 2000, The efficient market hypothesis, the gaussian assumption, and the investment management industry, EFMA 2001 Lugano Meetings

Weinan, 2017, A proposal on machine learning via dynamical systems, Commun. Math. Stat., 5, 1, 10.1007/s40304-017-0103-z

Van der Wilk, 2017

Williams, 2014, Data fusion via intrinsic dynamic variables: an application of data-driven koopman spectral analysis, EPL (Europhys. Lett.), 109

Xiao, 2019, A reduced order model for turbulent flows in the urban environment using machine learning, Build. Environ., 148, 323, 10.1016/j.buildenv.2018.10.035

Yu, 2020

Zhu, 1997, Algorithm 778: L-bfgs-b: Fortran subroutines for large-scale bound-constrained optimization, ACM Trans. Math. Softw., 23, 550, 10.1145/279232.279236

Zhu, 2019, Model error correction in data assimilation by integrating neural networks, Big Data Mining Anal., 2, 83, 10.26599/BDMA.2018.9020033

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Journal of Computational Science

Tập 58

101525

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